Autoimmune diseases are difficult to diagnose, as the symptoms can be nonspecific. A single test that could readily distinguish between an autoimmune and non-autoimmune disorder would allow physicians to focus efforts on the specific disease that affects the patient. This is critical for diabetes mellitus (DM), which has two subsets, Type I and Type II. The subsets of DM differ in pathogenesis, treatment and prognosis. Using microarray technology, we have compared differences in gene expression in peripheral blood mononuclear cells among patients with autoimmunity, including type I DM, and patients with non-autoimmune Type II DM. In Phase I, we investigated differences in gene expression between the DM types in adults and children. The findings from these studies suggest that we can readily identify subgroups of patients based on the gene expression patterns. The goal of our phase II application is to take these diagnostic tests closer to the marketplace. We have the following four specific aims: I. We will expand our microarray analysis using a larger cohort of individuals with diabetes from different racial backgrounds (Caucasian, African-American, and Hispanic) and different geographical locations to validate our results and to further test the notion that two classes of type I diabetes exist in the human population. II. We propose to validate identity of clones that exhibit extremes in hybridization among the control and different disease groups and develop "mini-microarray" and quantitative PCR tests that distinguish among the different disease groups. III. We will test these platforms by analyzing blood samples from a large cohort of control and diabetic individuals. IV. We will validate the diagnostic test in a cohort of individuals with an initial diagnosis of hyperglycemia. Long-term goals are to use results from microarray experiments to develop tests that have predictive value for the therapeutic management of individuals with autoimmune and non-autoimmune diseases. These include tests that classify diseases, predict severity, and predict optimal therapeutic options.